A dynamic approach for aircraft assignment and maintenance scheduling by airlines

The problems of assigning planes to flights and of fleet maintenance operations scheduling are considered in this paper. While recent approaches make use of artificial intelligence techniques running on main frame computers to solve combinatorial optimization problems for nominal operations, a dynamic approach is proposed here to face on-line operation conditions. The proposed solution mixes a Dynamic Programming approach (to cope with the fleet assignment problem) and a heuristic technique (to solve the embedded maintenance schedule problem). When applied to a medium charter airline, this approach shows acceptability characteristics for operational staffs, while providing efficient solutions. The proposed solution scheme can be considered as the basis for the development of an on-line decision support system for fleet operations management within airlines.     

Aircraft line maintenance scheduling and optimisation

With the advent of new technologies and more modern aircraft, many of the maintenance jobs traditionally scheduled for periodic block checks can now be performed in the ‘‘line maintenance” environment, i.e., during layovers between scheduled flights of an aircraft. This flexibility can be exploited to reduce maintenance costs and improve fleet utilisation of an airline. In this paper we introduce and study the Line Maintenance Scheduling Problem (LMSP). The LMSP assigns jobs to available maintenance opportunities, defined by aircraft routes, and sets the starting time for each job. Its objective is to minimise the deviation from this schedule with respect to given due dates for each task, without exceeding resource capacity at the airports at any moment. We formulate the LMSP as a mixed integer programming problem, and describe and compare two solution approaches for this problem: an integrated exact solution algorithm, which solves job assignment and timetabling simultaneously, and a sequential, heuristic approach. We tested our algorithms on a set of instances inspired on data provided by an industry partner. Our experiments show the applicability of both approaches on realistic settings: the exact approach was able to find the optimal solution for all instances, in less than 10 min on average. Our analysis also shows with an example that line maintenance can be more efficient when capacity is spatially spread, even if the total capacity is reduced.     

Berth scheduling by customer service differentiation: A multi-objective approach

In this paper the discrete and dynamic berth allocation problem is formulated as a multi-objective combinatorial optimization problem where vessel service is differentiated upon based on priority agreements. A genetic algorithms based heuristic is developed to solve the resulting problem. A number of numerical experiments showed that the heuristic performed well in solving large, real life instances. The heuristic provided a complete set of solutions that enable terminal operators to evaluate various berth scheduling policies and select the schedule that improves operations and customer satisfaction. The proposed algorithm outperformed a state of the art metaheuristic and provided improved results when compared to the weighted approach.     

A stochastic programming approach for floods emergency logistics

This article presents a model to assist decision makers in the logistics of a flood emergency. The model attempts to optimize inventory levels for emergency supplies as well as vehicles’ availability, in order to deliver enough supplies to satisfy demands with a given probability. A spatio-temporal stochastic process represents the flood occurrence. The model is approximately solved with sample average approximation. The article presents a method to quantify the impact of the various intervening logistics parameters. An example is provided and a sensitivity analysis is performed. The studied example shows large differences between the impacts of logistics parameters such as number of products, number of periods, inventory capacity and degree of demand fulfillment on the logistics cost and time. This methodology emerges as a valuable tool to help decision makers to allocate resources both before and after a flood occurs, with the aim of minimizing the undesirable effects of such events.     

A dynamic network efficiency measurement of airports performance considering sustainable development concept: A fuzzy dynamic network-DEA approach

In this paper, sustainability of airports is considered through a multi-perspective, multi-system, and multi-process operation. It is explored how an extension of fuzzy dynamic network performance measurement approach helps to determine the efficiency performance of an airport system. In this way, a three-pronged approach is intended which considers the internal functions of an airport, functions affecting the community and functions related to the passengers simultaneously. This novel combination makes it possible to have a comprehensive evaluation of airports' performance. Besides, fuzzy extension of SBM dynamic network approach makes it possible to deal better with the vagueness of variables during analysis. So, this extension is valuable from both technical and conceptual aspects which in turn provide useful information and insights for the future design of holistic strategies connected with sustainable development of airports where ever they are.     

The generalized lock scheduling problem: An exact approach

The present paper introduces an integrated approach to solving the generalized lock scheduling problem. Three interrelated sub problems can be discerned: ship placement, chamber assignment and lockage operation scheduling. In their turn, these are closely related to the 2D bin packing problem, the assignment problem and the (parallel) machine scheduling problem respectively. In previous research, the three sub problems mentioned were considered separately, often using (heuristic) interaction between them to obtain better solutions. A mixed integer linear programming model is presented and applied to instances from both inland locks and locks in a tide independent port. The experiments show that small instances incorporating a wide range of real-life constraints can be solved to optimality.     

Analysis of intermodal freight from China to Indian Ocean: A goal programming approach

This paper presents an intermodal network optimization model to examine the competitiveness of 36 alternative routings for freight moving from China to and beyond Indian Ocean. The proposed model, which is built upon the principles of goal programming, is able to handle multiple and conflicting objective functions such as minimizing transportation cost, transit time and transit time variability while ensuring flow continuity and transit nodes compatibility among the rail, road, ocean vessel, airplane and inland waterway transports. Transportation time and cost obtained from comprehensive industry sources are then fed into the intermodal transport network connecting two important Chinese origins and four Indian destinations, from which the most attractive routes are identified. In addition, the paper investigates several non-dominated transportation cases through sensitivity analysis tests, and analyzes the potential competitiveness and possible influences of future route developments to current transportation patterns in Asia.     

Experimental approach to NextGen benefits estimation: A case of single-airline Aircraft Arrival Management System

The Next Generation Air Transportation System (NextGen) embraces multiple operational and technological improvements that enable efficiencies for users and service providers. In managing the development of NextGen, the Federal Aviation Administration (FAA) is assessing initiatives and potential components of the future system. One component is software that jointly optimizes the timing of arriving traffic to streamline flight arrivals at congested airports (Aircraft Arrival Management Systems or AAMS). Since most of the proposed NextGen improvements are not yet implemented, their evaluations typically are conducted through modeling, simulation, and/or subject matter expert surveys. In contrast, this study utilizes experimental settings in a real-time operating environment to examine potential benefits of the AAMS. During the experiment, a number of system performance variables were recorded in two data collection periods: a passive period, when the system was operating without the AAMS, and an active period, when the AAMS optimized arriving traffic of a dominant airline. Due to confounding data issues, the results indicate that with only 6.5 percent of arriving traffic optimized, there are no observable improvements in the overall system performance. However, the study documents tangible benefits for optimized flights and a positive interaction effect between the FAA's Traffic Management Advisor and the AAMS.     

Assessing transport related social exclusion using a capabilities approach to accessibility framework: A dynamic Bayesian network approach

Accessibility is considered to be a valuable concept that can be used to generate insights on issues related to social exclusion due to limited access to transport options. Recently, researchers have attempted to link accessibility with popular theories of social justice such as Amartya Sen's Capabilities Approach (CA). Such studies have set the theoretical foundations on the way accessibility can be expressed through the CA, however, attempts to operationalise this approach remain fragmented and predominantly qualitative in nature. In this study, a novel framework of expressing accessibility at the level of an individual is proposed, based on the basic elements of the CA. In particular, dynamic Bayesian networks are used to express the causal relationship between capabilities, functionings, personal and environmental characteristics. This is done by introducing informative Dirichlet prior distributions constructed using data from traditional mobility surveys, modelling the transition probabilities with data related to place based characteristics and defining an observation model from unlabelled mobility data and places of interest (POI). We demonstrate the usefulness of the proposed framework by assessing the equality levels and their link to transport related social exclusion of different population groups in London, using unlabelled, service provider generated mobility data.     

Specification for a dynamic vehicle routing and scheduling system

In an e-commerce environment, order fulfilment is driven by customer demands and expectations. A dynamic vehicle routing and scheduling system may be specified which allows e-commerce customers to select their own delivery Time Windows and have these confirmed on-line as they place their order. The methodology is based upon demand forecasting, which leads to the generation of phantom orders and phantom routes. Subsequently, actual orders substitute for phantom orders in an on-line customer order process. The routing and scheduling method includes using both parallel tour-building and parallel insertion algorithms. Customer service levels are confirmed using GPS tracking and tracing, and a feedback loop uses expert systems or artificial intelligence as an input to the demand forecasting data to restart the whole process.     

A heuristic approach for solving an integrated gate reassignment and taxi scheduling problem

Capacity limitation of airport ground operation is one of the major limiting factors in air traffic operation. The congestion on the gate and taxiway causes severe delay and propagate effect on the flight schedule. This paper considers the problem of integrated gate reassignment and taxiway scheduling, in which complex constraints related to runway restriction, gate allocation and taxiway conflict are all incorporated when determining the schedule. To solve this problem, we propose a novel heuristic approach. First, all possible aircraft schedules are enumerated by disretizing the waiting time along the path. Then, the cost is evaluated for each schedule and the conflict detection is conducted to generate constraint sets. Finally, we propose a set partition model, in which each decision variable denotes a candidate schedule that takes into account the possible constraints when generated. This method is compared with a sequential method that solves gate reassignment and taxiway scheduling problem separately. Computational results highlight the strength of our method.     

Aircraft atypical approach detection using functional principal component analysis

In this paper, a post-operational detection method based on functional principal component analysis and clustering is presented and compared with regard to designed operational criteria. The methodology computes an atypical scoring on a sliding window. It enables not only to detect but also to localize where trajectories deviate statistically from the others. The algorithm is applied to the total energy management, estimated from ground-based data, during approach and landing. The detected atypical flights show non-nominal energy behaviors such as glide interceptions from above or high speed approaches. This promising methodology could help to enhance flight data analysis and safety, highlighting non-monitored behaviors.     

An integer programming approach to the bloodmobile routing problem

Every day, a blood center must determine a set of locations among a group of potential sites to route their vehicles for blood collection so as to avoid shortfalls. In this study, a vehicle routing problem is modeled using an integer programming approach to simultaneously identify number of bloodmobiles to operate and minimize the distance travelled. Additionally, the model is extended to incorporate uncertainty in blood potentials and variable durations in bloodmobile visits. Optimal routings are determined using CPLEX solver and branch-and-price algorithm. Results show that proposed algorithm solve the problem to optimality up to 30 locations within 3600 s.     

A multi-objective approach to scheduling joint participation with variable space and time preferences and opportunities

Activity scheduling supports activity-based analysis in travel demand management and promotes a potentially popular traveler assistance service. A multi-objective approach is proposed to schedule joint participation of multiple individuals, in which the candidate space-time opportunities for joint participation are identified by a concept of time-varying network-based prisms, and optimal opportunities for joint participation are determined by the non-dominated sorting genetic algorithm-II (NSGA-II) with four objectives (i) minimizing cost for congestion charges, (ii) maximizing participation desirability of time-of-day, (iii) minimizing total travel distance and (iv) time in the trips of multiple individuals. A scenario of joint participation among four people is designed and implemented to demonstrate the feasibility of this approach. The results suggest that this approach has the ability to schedule activities within real situations.     

2-Stage stochastic programming approach for hub location problem under uncertainty: A case study of air network of Iran

In this paper 2-stage stochastic programming has been developed for formulating stochastic uncapacitated multiple allocation hub location problem. This problem is studied under three cases. The first, stochastic demand, the second, stochastic transportation cost and the third, integrated stochastic, which is compounded of first and second cases. A case of air network in Iran is used to evaluate proposed formulations and computational results obtained by GAMS are presented. The results show that considering uncertainty into formulation could cause in different solutions.     

Stochastic programming approach to re-designing a warehouse network under uncertainty

The warehouse network re-design problem includes integrating or eliminating existing warehouses and establishing new sites. In this paper, we incorporate variability in product demand and operational costs with a two-stage stochastic modeling approach. We use the Sample Average Approximation (SAA) approach together with Benders decomposition to provide a solution method. Our results indicate not only that the stochastic solution is an improvement over the deterministic solution but also that the solutions’ differences grow with increasing uncertainty. The stochastic solutions show more robustness than the deterministic solutions. The computational results show that a change in the type of probability distribution of the stochastic parameters does not significantly affect the value of the stochastic solutions.     

Achieving genuinely dynamic road user charging: issues with a GNSS-based approach

A variety of approaches to road user charging (RUC) for reducing congestion and raising revenue to maintain and improve transport infrastructure is in place in many countries; examples of such RUC include: an Electronic Fee Collection System in Singapore, Cordon Pricing in Oslo City in Norway, Zoned Based Pricing in London and Distance-Based Pricing (also called Pay-As-You-Drive) in Germany and Switzerland. With the development of satellite technologies, the introduction of dynamic pricing becomes possible, affording an opportunity for RUC to fully reflect the ‘Polluter Pays Principle’. This paper provides critical and comparative assessments of existing road user charging (RUC) systems with reference to technological limitations and public and political acceptability. The paper then goes on to demonstrate a system architecture for a Global Navigation Satellite System (GNSS) based dynamic road user charging system capable of considering dynamic variables. Finally we consider the feasibility of the proposed system in relation to technology readiness and public acceptability. We then consider some potential wider benefits from the introduction of a comprehensive system that could be highlighted to justify the cost of development and implementation as well as to improve public and political acceptability. The paper ends with conclusions and future research directions.     

Truck and trailer scheduling in a real world,dynamic and heterogeneous context

We present a new variant of the Vehicle Routing Problem based on a real industrial scenario. This VRP is dynamic and heavily constrained and uses time-windows, a heterogeneous vehicle fleet and multiple types of job. A constructive solver is developed and tested using dynamic simulation of real-world data from a leading Scottish haulier. Our experiments establish the efficiency and reliability of the method for this problem. Additionally, a methodology for evaluating policy changes through simulation is presented, showing that our technique supports operations and management. We establish that fleet size can be reduced or more jobs handled by the company.     

A multi-population genetic algorithm for transportation scheduling

This study considers the integration of production and transportation scheduling in a two-stage supply chain environment. The objective function minimizes the total tardiness and total deviations of assigned work loads of suppliers from their quotas. After modeling the problem as a mixed integer programming problem, a genetic algorithm with three populations, namely, a multi-society genetic algorithm (MSGA), is proposed for solving it. MSGA is compared with the optimum solutions for small problems and a heuristic and a random search approach for larger problems. Additionally, an MSGA is compared with a generic genetic algorithm. The experimental results show the superiority of the MSGA.